جست‌وجوی استاد و پژوهشگر

Bryan A. Tolsonمشاهده پروفایل

استاد

• Environmental and water resources systems analysis
• Heuristic algorithms for optimization
• Uncertainty estimation
• +۷ مورد دیگر

Bryan A. Tolson is a Professor in the Department of Civil and Environmental Engineering at the University of Waterloo since 2005, promoted to Professor in 2020. His research focuses on environmental and water resources systems analysis, multi-objective optimization algorithms, and uncertainty/risk estimation in hydrological modeling. Developed the PA-DDS and HD-DDS optimization frameworks Leadership in Great Lakes water level management and distributed watershed modeling Collaborative work with Environment and Climate Change Canada on GEM-Hydro and MESH models His publications (141+) emphasize: Calibration efficiency for computationally intensive models Machine learning (LSTM, ReNN) integration in hydrology Data assimilation techniques (EnKF, SMOS soil moisture) Climate change impacts on water resources Infrastructure resilience (e.g., leak detection algorithms) Supervision includes doctoral students working on: Hydrological model calibration (Juliane Mai, Masoud Asadzadeh) Flood forecasting (Hongli Liu, Mahyar Shafii) GIS-based watershed delineation (Ming Han) Deep learning applications (Qiutong Yu)

Angelos Alexopoulosمشاهده پروفایل

استادیار

• Theoretical and Applied Econometrics
• Bayesian Analysis
• Computational Statistics
• +۴ مورد دیگر

Angelos Alexopoulos is an Assistant Professor at the Department of Economics , Athens University of Economics and Business . He has held Research Associate positions at the University of Cambridge, University College London, and University of Exeter in the UK. PhD: Athens University of Economics and Business Research Focus: Computational Statistics, Econometrics, Bayesian Analysis, Network Modelling Publications span Bayesian inference, epidemic forecasting, machine learning for fraud detection, and econometric methodology. Key journals include Journal of the Royal Statistical Society , Journal of Computational and Graphical Statistics , and Statistics and Computing . 2024: Gaussian invariance in MCMC 2024: Epidemic nowcasting models 2023: VAT fraud detection with ML Awards include certifications in Deep Learning (Coursera), Blockchain (edX), and Object-Oriented R Programming (DataCamp).

• Real-Time Systems
• Parallel Computing
• Cyber-Physical Systems
• +۳ مورد دیگر

Jing Li is an Assistant Professor at the Department of Computer Science in the Ying Wu College of Computing at New Jersey Institute of Technology. She holds a Ph.D. in Computer Science from Washington University in St. Louis (2017), advised by Chenyang Lu and Kunal Agrawal. Ph.D. in Computer Science, Washington University in St. Louis (2017) M.S. in Computer Science, Washington University in St. Louis (2014) B.S. in Computer Science, Harbin Institute of Technology, China (2011) Her research spans Real-Time Systems , Parallel Computing , Reinforcement Learning for System Design , and Scheduling Theory . Current projects include NSF-funded work on real-time systems with parallel resources and ARPA-E/IBM collaborations on reinforcement learning for converter design. Recent publications focus on AI-driven scheduling , parallel task optimization , and reinforcement learning applications in traffic control and circuit design. Key venues include AAAI, RTSS, PPoPP, and RTAS. Outstanding Achievement in Research (2022) Outstanding Paper Awards at RTSS (2018), RTAS (2016), ECRTS (2013) Turner Dissertation Award (2017) She advises graduate students in real-time systems and parallel computing, mentors NSF/ARPA-E projects, and leads professional services as TPC member and workshop organizer.

Chengnian Sunمشاهده پروفایل

دانشیار

• Software Engineering
• Programming Languages
• Compiler Testing
• +۴ مورد دیگر

Chengnian Sun is an Associate Professor at the Cheriton School of Computer Science , University of Waterloo, Canada. His research focuses on software engineering and programming languages with an emphasis on software reliability and programming productivity. Education : Ph.D. in Computer Science from National University of Singapore (2013) His work spans compiler testing (EMI, Dfusor, Kitten), program reduction (Perses, Vulcan, PPR), Android testing, and DNN testing. He has received multiple grants including Google Research Scholar Program (2025) and NSERC Discovery Grants (2024-2029). His recent publications focus on LLM-based compiler testing, weighted delta debugging, and ransomware resilience. Scientific Awards : Most Influential Paper Award at SANER (2022) NUS Research Scholarship (2008-2012) ACM SIGSOFT Distinguished Paper Award at ASE (2012) IBM Cup Campus Innovation Contest First Prize (2005) He advises Ph.D. and MMath students in software engineering, compiler testing, and program analysis, including several who have contributed to top-tier conferences like ICSE, ISSTA, and ASPLOS. His service includes program committee roles in ICSE, OOPSLA, and ISSTA.

• Composite Materials
• Additive Manufacturing
• Polymer Engineering
• +۳ مورد دیگر

R. Byron Pipes is the John L. Bray Distinguished Professor of Engineering at Purdue University's College of Engineering, holding affiliations with the Departments of Aeronautics and Astronautics, Chemical Engineering, and Materials Engineering. His research focuses on advanced composite materials, additive manufacturing, polymer processing, and the development of novel manufacturing techniques for high-performance materials. He is particularly known for contributions to composite structure optimization, thermoplastic composites, and material characterization via computational and experimental methods. Dr. Pipes' work integrates multi-scale modeling, digital thread integration, and machine learning to address challenges in composite manufacturing, including fiber orientation control, thermal management, and defect prediction. His research bridges fundamental material science with applied engineering solutions, emphasizing sustainability through upcycling strategies and energy-efficient processes. He has pioneered methodologies for compression molding of prepreg platelet composites, stamp forming of thermoplastics, and embedded heating systems in additive manufacturing. His academic leadership includes roles on committees such as the Dean's Awards Committee, reflecting his institutional contributions. Scientific recognition includes the prestigious John L. Bray Distinguished Professorship, underscoring his impact on engineering education and research. His lab focuses on translating innovations from computational simulations to real-world manufacturing applications, with a strong emphasis on aerospace and automotive material systems.

John Galeottiمشاهده پروفایل

پژوهشگر

• Medical Image Analysis
• Machine Learning Applications in Biomedical Imaging
• Medical Robotics
• +۳ مورد دیگر

John Galeotti is a Senior Systems Scientist and Adjunct Assistant Professor at Carnegie Mellon University (CMU), affiliated with the Robotics Institute (RI) and Biomedical Engineering departments. He directs the Biomedical Image Guidance Laboratory and has an adjunct appointment at the University of Pittsburgh's Bioengineering department. His research focuses on integrating medical robotics, computer vision, and advanced image analysis to improve clinical outcomes. With a Ph.D. in Robotics and B.S./M.S. in Computer Engineering, Galeotti's work emphasizes real-time computer-controlled optics, medical image segmentation, and AI-driven diagnostics. He has led projects in autonomous ultrasound scanning, needle insertion robotics, and synthetic data generation for medical AI. Research Keywords: Medical Robotics, Machine Learning, Ultrasound Segmentation, Computer Vision, Augmented Reality, Diagnostic Imaging Galeotti is a key contributor to the open-source Insight Segmentation and Registration Toolkit (ITK) and has received external teaching funding and awards for his internationally recognized course on medical image analysis algorithms. His lab's publications highlight advancements in AI applications for lung ultrasound, vessel segmentation, and optical coherence tomography.

Jiang Kanمشاهده پروفایل

مدرس

• Computer Science
• Artificial Intelligence
• Sports Analytics
• +۴ مورد دیگر

Jiang Kan is a Lecturer at the Department of Computer Science, National University of Singapore. He earned his Ph.D. in Computer Science from NUS in 2023, following an MComp (2017) and B.Sc. (1994) from NUS and Shanghai Jiao Tong University respectively. His teaching portfolio includes courses like Introduction to Computing , Introduction to Programming , Database Systems and Management , and Systems Programming . Education: Ph.D., Computer Science, National University of Singapore, 2023 M.Comp., National University of Singapore, 2017 B.Sc., Shanghai Jiao Tong University, 1994 Research Focus: Jiang Kan specializes in sports analytics, integrating computer vision and probabilistic modeling to analyze sports strategies, player dynamics, and broadcasting data. His work includes tennis and soccer strategy analysis , event recognition in sports videos , and injury prediction models . Publication Trends: Recent articles (2023-2025) explore hybrid approaches combining deep learning with probabilistic model checking for sports analytics. Key contributions include automated court detection , fine-grained event analysis , and dynamic team strategy modeling in tennis and soccer. Teaching: He teaches foundational and advanced courses in programming, computer organization, software engineering, and databases to both full-time and part-time students.

Mike Domaratzkiمشاهده پروفایل

دانشیار

• Bioinformatics
• Genomics
• Theoretical Computer Science
• +۳ مورد دیگر

Mike Domaratzki is an Associate Professor and Chair of the Department of Computer Science at Western University. His research focuses on bioinformatics, genomics, and theoretical computer science, with recent emphasis on machine learning tools for genomic prediction in crops. He has held academic roles at Western University and previously at the University of Manitoba and Acadia University. Education: (No explicit details provided in texts) Research Interests: Domaratzki’s work bridges computational methods and biological systems, including algorithm design for genomic analysis, machine learning applications in agriculture, and theoretical foundations of computing. His recent projects address challenges in imbalanced data classification and crop yield prediction using advanced neural networks. Teaching: He has taught a wide range of courses, including introductory programming, data structures, algorithms, bioinformatics, and automata theory across multiple institutions. Notable courses include COMPSCI 1026/1027 at Western, and COMP courses at the University of Manitoba/Acadia covering foundational CS topics and specialized areas like bionformatics algorithms. Publications: His work spans machine learning, genomics, and theoretical computer science, with recent trends emphasizing agricultural genomic applications and data-driven health analytics. Key themes include imbalanced data solutions, neural network architectures for genomics, and computational tools for biological data interpretation. Grants/Advising: No specific grants or student advising details are listed, though his teaching and research imply active involvement in mentoring. His lab focuses on interdisciplinary projects combining computational methods with biological datasets.

Timo Gerkmannمشاهده پروفایل

استاد

• Statistical Signal Processing
• Machine Learning
• Speech and Audio Processing
• +۳ مورد دیگر

Prof. Timo Gerkmann is a Professor at the University of Hamburg's Department of Informatics, leading the Signal Processing Research Group. His research focuses on statistical signal processing and machine learning for speech and audio applications, including communication devices, hearing aids, audiovisual media, and human-machine interfaces. He previously held roles at Technicolor Research & Innovation, KTH Royal Institute of Technology, and Siemens Corporate Research. His work emphasizes generative models, diffusion-based approaches, and acoustic signal enhancement. He currently serves as Senior Area Editor of the IEEE/ACM Transactions on Audio, Speech, and Language Processing. Research Interests: Statistical Signal Processing for Speech and Audio Machine Learning Applications in Acoustic Environments Diffusion Models for Audio Restoration Audio-Visual Speech Enhancement Human-Machine Interaction Systems Acoustic Scene Analysis Publications Highlight Trends: Recent works focus on diffusion models for speech enhancement, generative approaches to dereverberation, and audiovisual multimodal analysis. He has pioneered frameworks like ReverbFX datasets and FlowDec codecs, emphasizing perceptual quality and unsupervised domain adaptation. Advising & Grants: While no specific students or grants are listed, his research group actively publishes in top venues, indicating sustained academic contributions. His work bridges theoretical signal processing with applied systems engineering. Labs/Teams: Leads the Signal Processing (SP) Research Group at UHH, specializing in cutting-edge audio technologies and human-centric signal processing solutions.

Michael Möllerمشاهده پروفایل

استاد

• Computer Vision
• Machine Learning
• Inverse Problems
• +۲ مورد دیگر

Michael Möller is a Professor at the University of Siegen, leading the Lehrstuhl für Computer Vision within the Institute for Vision and Graphics. His research spans computer vision, machine learning, and optimization, with applications in medical imaging and computational geometry. Research Interests: Focuses on solving inverse problems in imaging, neural architecture optimization, and quantum-hybrid computational methods. His work integrates deep learning with traditional optimization for scalable solutions in 3D reconstruction and image analysis. Publications: Recent articles explore advancements in neural architecture search robustness, quantum annealing for shape matching, and data-driven regularization techniques for CT reconstruction, reflecting cross-disciplinary applications.

Dario Granaمشاهده پروفایل

استاد

• Rock Physics
• Seismic Reservoir Characterization
• Inverse Problems
• +۱ مورد دیگر

Dr. Dario Grana is the Wyoming Excellence Chair and Professor at the University of Wyoming's Department of Geology and Geophysics. His research focuses on rock physics, seismic reservoir characterization, inverse problems, and geostatistics, with applications in critical zone studies, CO2 sequestration monitoring, and subsurface characterization. Education includes: PhD and MS in Geophysics, Stanford University (2013) MS in Applied Mathematics, University of Milano-Bicocca (2006) BS and MS in Mathematics, University of Pavia (2003, 2005) Research explores rock physics modeling in critical zones, geophysical monitoring of CO2 storage, joint seismic-electromagnetic inversion, and stochastic methods for subsurface characterization. Recent publications emphasize machine learning integration with geostatistical inversion and Bayesian methods for uncertainty quantification. Teaching includes Quantitative Geoscience, Mathematical Methods for Geoscience, Exploration Geoscience, Inverse Theory, and Rock Physics.

Liangxiu Hanمشاهده پروفایل

استاد

• Big Data Analytics
• Artificial Intelligence
• Machine Learning
• +۵ مورد دیگر

Professor Liangxiu Han is Professor of Computational Science at Manchester Metropolitan University's Department of Computing and Mathematics, where she also serves as co-Director of the Centre for Advanced Computational Science and Deputy Director of the ManMet Crime and Well-Being Big Data Centre. Her research focuses on developing novel approaches to big data analytics, machine learning, and AI, with applications in precision agriculture, healthcare, smart cities, and cybersecurity. Professor Han's research integrates fundamental and applied work in large-scale data processing, intelligent management of distributed systems, and mathematical modeling. Her current activities aim to provide intelligent ICT-based solutions for societal challenges in food security, healthcare, and sustainable urban development. Her recent publications demonstrate leadership in applying AI to medical challenges like Alzheimer's drug discovery and glaucoma diagnosis. The research combines Bayesian deep learning, computational chemistry, and neuropharmacology to develop innovative therapeutic approaches and diagnostic tools.

Stefan Radevمشاهده پروفایل

استادیار

• Bayesian Inference
• Deep Learning
• Probabilistic Modeling
• +۳ مورد دیگر

Stefan Radev is an Assistant Professor in the Cognitive Science department at Rensselaer Polytechnic Institute . His work focuses on developing Bayesian methods with generative neural networks and computational models for complex systems like cognition and disease outbreaks. He is the core developer of the BayesFlow framework, which enables amortized Bayesian inference using deep learning. Radev's research addresses computational challenges in Bayesian workflows, such as rapid parameter estimation and model validation through neural networks. His primary research interests include Deep Learning , Probabilistic Modeling , and their applications in computational neuroscience and biomedical engineering. He collaborates with the Center for Modeling, Simulation and Imaging in Medicine (CEMSIM) and contributes to open-source projects like BayesFlow, which supports multi-backend frameworks (PyTorch/TensorFlow/JAX). Recent publications highlight advancements in amortized inference, simulation-based calibration, and robust Bayesian workflows. His work spans domains from cognitive modeling to biomedical imaging, emphasizing interdisciplinary applications of Bayesian methods. No scientific awards are explicitly listed, though his contributions to open-source tools and impactful research indicate significant academic recognition.

James Fouldsمشاهده پروفایل

دانشیار

• Machine Learning
• Artificial Intelligence
• Ethics in AI
• +۴ مورد دیگر

James Foulds is an Associate Professor in the Department of Information Systems at the University of Maryland, Baltimore County (UMBC). His research focuses on socially conscious machine learning and artificial intelligence, emphasizing fairness, privacy, and computational social science using probabilistic models and Bayesian inference. He holds an NSF CAREER Award and a runner-up prize from the SoCal Machine Learning Symposium (2016). His work includes developing fair AI systems, privacy-preserving techniques, and Bayesian modeling approaches for intersectional fairness. Dr. Foulds earned his postdoctoral training at UC San Diego and UC Santa Cruz, and completed his PhD at UC Irvine. He teaches courses in artificial intelligence, ethical AI, and data mining. His research has been supported by grants such as the NSF CISE Research Initiation Initiative (CRII) and the NSF EAGER grant for healthcare resource fairness. Notable contributions include the Data Mining textbook (under revision), the Stochastic-CVB0 Julia package for topic modeling, and collaborations on AI fairness frameworks like the IEEE DEB Special Issue. He advises students on projects in fair machine learning, including equitable healthcare resource allocation and bias mitigation in resume filtering systems.

Fabien Moutardeمشاهده پروفایل

استاد

• Machine Learning
• Deep Learning
• Computer Vision
• +۸ مورد دیگر

Fabien Moutarde is a Full Professor and Director of the Center for Robotics at MINES ParisTech (PSL University, Paris, France). He holds a PhD in Physics and an Habilitation to Direct Research in Engineering Sciences. Dr. Moutarde coordinates French engineering education at ParisTech_Shanghai (SPEIT) in China. Research Areas Deep Learning & Reinforcement Learning Computer Vision for Intelligent Vehicles Collaborative Robotics Traffic Analysis & Forecasting Human Gesture Recognition Recent Article Trends His work focuses on autonomous driving using Deep Reinforcement Learning, pedestrian trajectory prediction with spatio-temporal attention, and multi-modal localization techniques combining vision with Wi-Fi. Key applications include urban traffic analysis, collaborative robotics, and end-to-end driving systems. Leadership & Teaching Co-created specialized Machine Learning courses Pioneered Deep Reinforcement Learning lectures Led French-Chinese academic coordination Former UML/Java curriculum developer Publications With 28 h-index, his research includes 30+ IEEE/ACM publications on autonomous vehicles, gesture recognition, and traffic mining. Representative conferences: CVPR, NeurIPS, IROS, ITSC.